Semantic Bird's Eye View (BEV) maps offer a rich representation with strong
occlusion reasoning for various decision making tasks in autonomous driving.
However, most BEV mapping approaches employ a fully supervised learning
paradigm that relies on large amounts of human-annotated BEV ground truth data.
In this work, we address this limitation by proposing the first unsupervised
representation learning approach to generate semantic BEV maps from a monocular
frontal view (FV) image in a label-efficient manner. Our approach pretrains the
network to independently reason about scene geometry and scene semantics using
two disjoint neural pathways in an unsupervised manner and then finetunes it
for the task of semantic BEV mapping using only a small fraction of labels in
the BEV. We achieve label-free pretraining by exploiting spatial and temporal
consistency of FV images to learn scene geometry while relying on a novel
temporal masked autoencoder formulation to encode the scene representation.
Extensive evaluations on the KITTI-360 and nuScenes datasets demonstrate that
our approach performs on par with the existing state-of-the-art approaches
while using only 1% of BEV labels and no additional labeled data.

该研究提出了一种无监督学习方法，从单眼正视图像生成语义鸟瞰地图，以在自动驾驶的各种决策任务中提供强大的遮挡推理能力，其使用了 1% 的标注数据和无额外标记数据，在 KITTI-360 和 nuScenes 数据集上表现与现有最先进的方法相当。